Inertial navigation of a platform is based upon the integration of specific forces and angular rates as measured by inertial sensors (e.g. accelerometer, gyroscopes) of a device containing the sensors and positioned within a motion-capable platform. In traditional systems, the device is tethered to the platform. Measurements from the device may be used to determine the position, velocity and attitude of the device and/or the platform.
Alignment of the inertial sensors within the platform (i.e. alignment of the device containing the sensors with the platform's forward, transversal and vertical axis) is typically required for traditional inertial navigation systems. Where the inertial sensors are not properly aligned, the positions and attitude calculated using measurements from the inertial sensors will not be representative of the state of the platform. As such, in order to achieve high accuracy navigation solutions, inertial sensors must be tethered within the platform and careful manual mounting of the device within the platform is needed.
Portable navigation devices (or navigation-capable devices), however, are able to move, whether constrained or unconstrained within the platform (such as for example a person, vehicle, or vessel of any type), and careful mounting or tethering of the device to the platform is not an option.
Existing portable navigation devices (or navigation-capable devices) cannot achieve accurate attitude and position of the platform unless at least one of the following three conditions is known:
1) absolute attitude angles for the device and the platform;
2) absolute attitude angles for the device and the misalignment between the device and platform;
3) absolute attitude angles for the platform and the misalignment between the device and platform.
Since the first above option need two assemblies of sensors at least, one on the device and one on the platform, knowledge of misalignment is a key factor to enable portable navigation devices without the previously mentioned constraint.
As navigation-capable devices (e.g. mobile/smart phones) become increasingly popular, they can come equipped with Assisted Global Positioning System (AGPS) chipsets having high sensitivity capabilities capable of providing absolute positioning of the platform (e.g. user) even in environments without a clear line of sight to satellite signals. In environments where AGPS information alone is not enough, such as deep indoors or in challenging downtown navigation or localization, one possible solution is to incorporate cell tower identification or, if possible, trilateration of cell towers for a position fix (where AGPS solution is unavailable). Despite these two known positioning methods available in many mobile devices, accurate indoor localization still presents a challenge and fails to satisfy the accuracy demands of current location based services (LBS). Additionally, these methods may only provide the absolute heading of the platform, without any information on the device's heading.
Mobile navigation-capable devices (e.g. mobile/smart phones) can come equipped with Micro Electro Mechanical System (MEMS) sensors that are used predominantly for screen control and entertainment applications. These sensors have not been broadly used to date for navigation purposes due to very high noise, large random drift rates, and frequently changing orientations of the device with respect to the platform.
Mobile devices can also come equipped with magnetometers, and in some cases, it has been shown that a navigation solution using accelerometers and magnetometers may be possible if the user is careful enough to keep the device in a specific orientation with respect to their body, such as when held carefully in front of the user after calibrating the magnetometer.
There is a need, however, for a method of providing a navigation solution that is capable of accurately utilizing measurements from a navigation-capable device within a platform, and thereby determining the navigation state of the device/platform without any constraints on the platform (i.e. in indoor or outdoor environments) or the mobility of the device within the platform. The estimation of the position and attitude of the platform should be independent of the usage of the device (e.g. the way the user is holding or moving the device during navigation). The needed method should allow the device to be tilted in any orientation while still providing seamless navigation information without degradation in performance.
In addition to the above mentioned application of portable devices (that include a full navigation solution including position, velocity and attitude, or position and attitude), there are other applications (that may include estimating a full navigation solution, or an attitude only solution or an attitude and velocity solution) where the needed method is aimed at enhancing the user experience and usability, and may be applicable in a number of scenarios such as, for example:
video gaming equipment;
augmented reality equipment; or
wrist watches.
Some techniques available in the literature are able only to calculate just discrete or pre-determined values of the misalignment angle based on discrete use case classification of the device. This limits their usage to these discrete use cases, and even when one of the supported use cases is used the accuracy can deteriorate if the true misalignment value is somewhat different then the discrete misalignment value assigned with the classified use case (the latter may happen a lot in real life scenarios).
To resolve this key problem of misalignment determination between the device and the pedestrian, a former method in literature uses Principle Component Analysis (PCA) to obtain the direction of the axis of motion (i.e. the axis of the forward-backward motion direction with a 180 degrees of ambiguity). This means that PCA alone can't detect the forward direction from the backward direction. The rationale behind using PCA to obtain the direction of the axis of motion is that the variance of the acceleration vector is minimum along the lateral axis of the human body and maximum along the forward axis of the human body. Based on this theory, the motion axis of the device with respect to the user motion can be estimated but with a 180 degrees of ambiguity as explained above. In some former literature, the 180 degrees ambiguity problem of the direction of motion is resolved based on the idea that the PCA is implemented to the projected horizontal acceleration to get the direction of motion and the integration over the component is used to determine which way is front (leads to positive). However, this method was developed for device in pocket and is not suitable for all other device orientations. Another method in the literature to solve the 180 degrees ambiguity and to determine the forward direction is by testing whether the slope of vertical acceleration at the peak of acceleration in the motion signal in the forward-backward direction is increasing; if so the direction of the motion axis is forward otherwise it is backward. This technique does not work correctly for all device usages and orientations.
As such, there is a need for a method and apparatus to resolve the 180 degrees ambiguity and to be able to work for any device usage or orientation with respect to the pedestrian, and for various people's gaits and speeds.